Exposure measurement errors in cancer epidemiology poses special methodologic challenges. For example, nutritional and physical activity patterns form the basis for many etiologic hypotheses concerning cancer. However, nutrient intake and physical activity are difficult to measure precisely. It is the role of measurement error correction methods to validly and efficiently estimate the relationship between exposures and cancer outcomes. To accomplish this requires both a main study where disease and the surrogate exposure are measured and validation data to determine the extent of the measurement error. In this proposal, we seek to further expand our group's previous work on methods of corrections for measurement error and misclassification. A major focus is on nutritional studies based on intakes and activity measures reported at multiple questionnaires over time when the target exposure is long-term average diet and/or activity. Here, the dependent variable is time to cancer incidence or mortality; thus, Cox models are considered in which time-varying covariates such as cumulative averages and cumulative exposures are of primary interest. Modeling and variable selection issues in the context of real-life applications of measurement error correction methods will be carefully studied, with an aim to provide guidance for current and future users of the methodology; no such guidance currently exists. Application of methods to correct for measurement error in time-varying exposures such as cumulative averages requires validation studies in which repeated measures within study participants are repeatedly validated. Analysis of the currently ongoing HSPH Lifestyle Validation Study' which will make available validation of up to four repeated dietary and physical activity measurements, using state of the art biomarker technology including doubly labeled water and urinary nitrogen, will be another major focus of our work. Finally, our attention will turn to an entirely new area of research in this cycle: methods which consider measurement error and misclassification in the development and evaluation of models for the prediction of breast, colon and ovarian cancer risk. As previously, user friendly , public use software development will be a focus of all new methods of development.